Biblio

Ultra high frequency (UHF) partial discharge detection technology has been widely used in on-line monitoring of electrical equipment, for the influence factors of UHF signal's transfer function is complicated, the calibration of UHF method is still not realized until now. In order to study the calibration influence factors of UHF partial discharge (PD) detector, the discharge mechanism of typical PD defects is analyzed, and use a PD UHF signal simulator with multiple adjustable parameters to simulate types of PD UHF signals of electrical equipment, then performed the relative experimental research in propagation characteristics and Sensor characteristics of UHF signals. It is concluded that the calibration reliability has big differences between UHF signal energy and discharge capacity of different discharge source. The calibration curve of corona discharge and suspended discharge which can representation the severity of equipment insulation defect more accurate, and the calibration curve of internal air gap discharge and dielectric surface discharge is poorer. The distance of UHF signal energy decays to stable period become smaller with increase of frequency, and the decay of UHF signal energy is irrelevant to its frequencies when the measuring angle is changing. The frequency range of measuring UHF signal depends on effective frequency range of measurement sensor, moreover, the gain and standing-wave ratio of sensor and the energy of the received signal manifested same change trend. Therefore, in order to calibration the UHF signal, it is necessary to comprehensive consideration the specific discharge type and measuring condition. The results provide the favorable reference for a further study to build the calibration system of UHF measuring method, and to promote the effective application of UHF method in sensor characteristic fault diagnosis and insulation evaluation of electrical equipment.

E- Health systems, specifically, Telecare Medical Information Systems (TMIS), are deployed in order to provide patients with specific diseases with healthcare services that are usually based on remote monitoring. Therefore, making an efficient, convenient and secure connection between users and medical servers over insecure channels within medical services is a rather major issue. In this context, because of the biometrics' characteristics, many biometrics-based three factor user authentication schemes have been proposed in the literature to secure user/server communication within medical services. In this paper, we make a brief study of the most interesting proposals. Then, we propose a new three-factor authentication and key agreement scheme for TMIS. Our scheme tends not only to fix the security drawbacks of some studied related work, but also, offers additional significant features while minimizing resource consumption. In addition, we perform a formal verification using the widely accepted formal security verification tool AVISPA to demonstrate that our proposed scheme is secure. Also, our comparative performance analysis reveals that our proposed scheme provides a lower resource consumption compared to other related work's proposals.

The advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) enabled a new class of smart and interactive devices. With their continuous connectivity and their access to valuable information in both the digital and physical world, they are attractive targets for security attackers. Hence, with their integration into both the industry and consumer devices, they added a new surface for cybersecurity attacks. These potential threats call for special care of security vulnerabilities during the design of IoT devices and CPS. The design of secure systems is a complex task, especially if they must adhere to other constraints, such as performance, power consumption, and others. A range of design space exploration tools have been proposed in academics, which aim to support system designers in their task of finding the optimal selection of hardware components and task mappings. Said tools offer a limited way of modeling attack scenarios as constraints for a system under design. The framework proposed in this paper aims at closing this gap, offering system designers a way to consider security attacks and security risks during the early design phase. It offers designers to model security constraints from the view of potential attackers, assessing the probability of successful security attacks and security risk. The framework's feasibility and performance is demonstrated by revisiting a potential system design of an industry partner.

E-learning enables the transfer of skills, knowledge, and education to a large number of recipients. The E-Learning platform has the tendency to provide face-to-face learning through a learning management system (LMS) and facilitated an improvement in traditional educational methods. The LMS saves organization time, money and easy administration. LMS also saves user time to move across the learning place by providing a web-based environment. However, a few students could be willing to exploit such a system's weakness in a bid to cheat if the conventional authentication methods are employed. In this scenario user authentication and surveillance of end user is more challenging. A system with the simultaneous authentication is put forth through multifactor adaptive authentication methods. The proposed system provides an efficient, low cost and human intervention adaptive for e-learning environment authentication and monitoring system.

The intensity of DNA figuring will reinforce the current security on frameworks by opening up another probability of a half and half cryptographic framework. Here, we are exhibiting the DNA S-box for actualizing cryptographic algorithm. The DNA based S-Box is designed using vivado software and implemented using Artix-7 device. The main aim is to design the DNA based S-box to increase the security. Also pipelining and parallelism techniques are to be implement in future to increase the speed.

This paper considers improving the confidentiality of the next generation of wireless communications by using the watermark-based blind physical layer security (WBPLSec) in Visible Light Communications (VLCs). Since the growth of wireless applications and service, the demand for a secure and fast data transfer connection requires new technology solutions capable to ensure the best countermeasure against security attacks. VLC is one of the most promising new wireless communication technology, due to the possibility of using environmental artificial lights as data transfer channel in free-space. On the other hand, VLCs are even inherently susceptible to eavesdropping attacks. This work proposes an innovative scheme in which red, green, blue (RGB) light-emitting-diodes (LEDs) and three color-tuned photo-diodes (PDs) are used to secure a VLC by using a jamming receiver in conjunction with the spread spectrum watermarking technique. To the best of the author's knowledge, this is the first work that deals with physical layer security on VLC by using RGB LEDs.

We consider a wireless communication system that consists of a transmitter, a receiver, and an adversary. The transmitter transmits signals with different modulation types, while the receiver classifies its received signals to modulation types using a deep learning-based classifier. In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter's signals to fool the classifier at the receiver into making errors. While this evasion attack has received growing interest recently, the channel effects from the adversary to the receiver have been ignored so far such that the previous attack mechanisms cannot be applied under realistic channel effects. In this paper, we present how to launch a realistic evasion attack by considering channels from the adversary to the receiver. Our results show that modulation classification is vulnerable to an adversarial attack over a wireless channel that is modeled as Rayleigh fading with path loss and shadowing. We present various adversarial attacks with respect to availability of information about channel, transmitter input, and classifier architecture. First, we present two types of adversarial attacks, namely a targeted attack (with minimum power) and non-targeted attack that aims to change the classification to a target label or to any other label other than the true label, respectively. Both are white-box attacks that are transmitter input-specific and use channel information. Then we introduce an algorithm to generate adversarial attacks using limited channel information where the adversary only knows the channel distribution. Finally, we present a black-box universal adversarial perturbation (UAP) attack where the adversary has limited knowledge about both channel and transmitter input. By accounting for different levels of information availability, we show the vulnerability of modulation classifier to over-the-air adversarial attacks.

Mobile Ad-hoc Network (MANET) is a dynamic network between mobile nodes for sharing of information and is popular for its infrastructure-less design. Due to the lack of central governing body, however, various security threats come forward in MANETs in comparison to its infrastructure based counterparts. Blackhole attack is one of the most challenging security issues present in MANETs. Blackhole attack reduces network efficiency considerably by disrupting the flow of data between source and destination. In this paper, we propose an algorithm which is based on the technique of changing the sequence number present in control packets, in particular the Route Reply Packets (RREP) in widely used Ad-Hoc On Demand Distance Vector (AODV) routing protocol, in order to identify the blackhole nodes and thereby to minimize the data loss by discarding the route with such Blackhole nodes. Simulation results show that the proposed algorithm outperforms the legacy Intrusion Detection System (IDS) provisioned for AODV.

In order to improve the concealment of image steganography, a new method is proposed. The algorithm firstly adopted GM (1, 1) model to detect texture and edge points of carrier image, then embedded secret information in them. GM (1, 1) model of optimized parameters can make full use of pixels information. These pixels are the nearest to the detected point, so it improves the detection accuracy. The method is a kind of steganography based on human visual system. By testing the stegano images with different embedding capacities, the result indicates concealment and image quality of the proposed algorithm are better than BPCS (Bit-plane Complexity Segmentation) and PVD (Pixel-value Differencing), which are also based on visual characteristics.

One of a high relentless attack is the crucial distributed DoS attacks. The types and tools for this attacks increases day-to-day as per the technology increases. So the methodology for detection of DDoS should be advanced. For this purpose we created an automated DDoS detector using ML which can run on any commodity hardware. The results are 98.5 % accurate. We use three classification algorithms KNN, RF and NB to classify DDoS packets from normal packets using two features, delta time and packet size. This detector mostly can detect all types of DDoS such as ICMP flood, TCP flood, UDP flood etc. In the older systems they detect only some types of DDoS attacks and some systems may require a large number of features to detect DDoS. Some systems may work only with certain protocols only. But our proposed model overcome these drawbacks by detecting the DDoS of any type without a need of specific protocol that uses less amount of features.

Cloud computing provides so many groundbreaking advantages over native computing servers like to improve capacity and decrease costs, but meanwhile, it carries many security issues also. In this paper, we find the feasible security attacks made about cloud computing, including Wrapping, Browser Malware-Injection and Flooding attacks, and also problems caused by accountability checking. We have also analyzed the honey pot attack and its procedural intrusion way into the system. This paper on overall deals with the most common security breaches in cloud computing and finally honey pot, in particular, to analyze its intrusion way. Our major scope is to do overall security, analyze in the cloud and then to take up with a particular attack to deal with granular level. Honey pot is the one such attack that is taken into account and its intrusion policies are analyzed. The specific honey pot algorithm is in the queue as the extension of this project in the future.

Using the blockchain technology to store the privatedocuments of individuals will help make data more reliable and secure, preventing the loss of data and unauthorized access. The Consensus algorithm along with the hash algorithms maintains the integrity of data simultaneously providing authentication and authorization. The paper incorporates the block chain and the Identity Based Encryption management concept. The Identity based Management system allows the encryption of the user's data as well as their identity and thus preventing them from Identity theft and fraud. These two technologies combined will result in a more secure way of storing the data and protecting the privacy of the user.

The wireless technology has knocked the door of tremendous usage and popularity in the last few years along with a high growth rate for new applications in the networking domain. Mobile Ad hoc Networks (MANETs) is solitary most appealing, alluring and challenging field where in the participating nodes do not require any active, existing and centralized system or rigid infrastructure for execution purpose and thus nodes have the moving capability on arbitrary basis. Radio range nodes directly communicate with each other through the wireless links whereas outside range nodes uses relay principle for communication. Though it is a rigid infrastructure less environment and has high growth rate but security is a major concern and becomes vital part of providing hostile free environment for communication. The MANET imposes several prominent challenges such as limited energy reserve, resource constraints, highly dynamic topology, sharing of wireless medium, energy inefficiency, recharging of the batteries etc. These challenges bound to make MANET more susceptible, more close to attacks and weak unlike the wired line networks. Theresearch paperismainly focused on two aspects, one is computation termination of cluster head algorithm and another is use of finite state machine for attacks identification.

The resistance to attacks aimed to break CAPTCHA challenges and the effectiveness, efficiency and satisfaction of human users in solving them called usability are the two major concerns while designing CAPTCHA schemes. User-friendliness, universality, and accessibility are related dimensions of usability, which must also be addressed adequately. With recent advances in segmentation and optical character recognition techniques, complex distortions, degradations and transformations are added to text-based CAPTCHA challenges resulting in their reduced usability. The extent of these deformations can be decreased if some additional security mechanism is incorporated in such challenges. This paper proposes an additional security mechanism that can add an extra layer of protection to any text-based CAPTCHA challenge, making it more challenging for bots and scripts that might be used to attack websites and web applications. It proposes the use of hidden text-boxes for user entry of CAPTCHA string which serves as honeypots for bots and automated scripts. The honeypot technique is used to trick bots and automated scripts into filling up input fields which legitimate human users cannot fill in. The paper reports implementation of honeypot technique and results of tests carried out over three months during which form submissions were logged for analysis. The results demonstrated great effectiveness of honeypots technique to improve security control and usability of text-based CAPTCHA challenges.

The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.

In the Mobile Ad hoc Network, the entire nodes taken as routers and contribute transmission when the nodes are not in the range of transmission for the senders. Directing conventions for the ad hoc systems are intended for the indisposed system setting, on the supposition that all the hubs in the system are reliable. Dependability of the directing convention is endangered in the genuine setting as systems are assaulted by pernicious hubs which regularly will in general upset the correspondence. Right now, it is proposed to contemplate the exhibition of the DSR convention under deceitful conditions. Another strategy is proposed to recognize untrue nodes dependent on the RREQ control parcel arrangement.

The concept of the adversary model has been widely applied in the context of cryptography. When designing a cryptographic scheme or protocol, the adversary model plays a crucial role in the formalization of the capabilities and limitations of potential attackers. These models further enable the designer to verify the security of the scheme or protocol under investigation. Although being well established for conventional cryptanalysis attacks, adversary models associated with attackers enjoying the advantages of machine learning techniques have not yet been developed thoroughly. In particular, when it comes to composed hardware, often being security-critical, the lack of such models has become increasingly noticeable in the face of advanced, machine learning-enabled attacks. This paper aims at exploring the adversary models from the machine learning perspective. In this regard, we provide examples of machine learning-based attacks against hardware primitives, e.g., obfuscation schemes and hardware root-of-trust, claimed to be infeasible. We demonstrate that this assumption becomes however invalid as inaccurate adversary models have been considered in the literature.

An acoustic fingerprint is a condensed and powerful digital signature of an audio signal which is used for audio sample identification. A fingerprint is the pattern of a voice or audio sample. A large number of algorithms have been developed for generating such acoustic fingerprints. These algorithms facilitate systems that perform song searching, song identification, and song duplication detection. In this study, a comprehensive and powerful survey of already developed algorithms is conducted. Four major music fingerprinting algorithms are evaluated for identifying and analyzing the potential hurdles that can affect their results. Since the background and environmental noise reduces the efficiency of music fingerprinting algorithms, behavioral analysis of fingerprinting algorithms is performed using audio samples of different languages and under different environmental conditions. The results of music fingerprint classification are more successful when deep learning techniques for classification are used. The testing of the acoustic feature modeling and music fingerprinting algorithms is performed using the standard dataset of iKala, MusicBrainz and MIR-1K.

Service providers typically utilize Web APIs to enable the sharing of tenant data and resources with numerous third party web, cloud, and mobile applications. Security mechanisms such as OAuth 2.0 and API keys are commonly applied to manage authorization aspects of such integrations. However, these mechanisms impose functional and security drawbacks both for service providers and their users due to their static design, coarse and context insensitive capabilities, and weak interoperability. Implementing secure, feature-rich, and flexible data sharing services still poses a challenge that many providers face in the process of opening their interfaces to the public.To address these issues, we design the framework that allows pluggable and transparent externalization of authorization functionality for service providers and flexibility in defining and managing security aspects of resource sharing with third parties for their users. Our solution applies a holistic perspective that considers service descriptions, data fragments, security policies, as well as system interactions and states as an integrated space dynamically exposed and collaboratively accessed by agents residing across organizational boundaries.In this work we present design aspects of our contribution and illustrate its practical implementation by analyzing case scenario involving resource sharing of a popular service.

Recently, border monitoring and security has become an important topic since current methods against illegal immigration are expensive and inefficient. In particular, inefficiency and ineffectiveness increase when monitoring operations are focused on complex borders, where there is no available/reliable connectivity. In the last decade, the deployment of different kinds of unmanned aerial vehicles was seen as the main paradigm to provide on-demand wireless network access. Significant research work has been done on so called mobile base stations. Nevertheless, drones have specific technical limitations in terms, for example, of battery life and carried weight. Given above fundamental limits, network virtualization becomes a fundamental paradigm for system realization. In the last years, baseband processing was not seen any more as a monolithic block but has been studied as a chain of virtual functions. Especially, baseband unit can be split into five sub-blocks belonging to layer 1 to layer 3, where each degree of splitting implies more and more stringent requirements to be guaranteed, mainly in terms of throughput and latency. Split E is the logic separation of hybrid automatic repeat request from lower layers, which imposes the most flexible requirements. On the other hand, Split D (forward error correction, encoding/decoding logic functions) sets more stringent bounds on throughput and latency so that it requires careful study and detailed analysis for a correct system-level design. The main objective of this article is to study theoretically and numerically (i.e. via simulations) Split D to make it feasible with the help of small satellites. The paper will study the structure and the capabilities of small satellites to be used as small data centers to host radio access virtual network functions like forward error correction. The theoretical analysis is supported by simulations in order to highlight advantages and challenges of the proposed approach.

Military supply chains play a critical role in the acquisition and movement of goods for defence purposes. The disruption of these supply chain processes can have potentially devastating affects to the operational capability of military forces. The introduction and integration of new technologies into defence supply chains can serve to increase their effectiveness. However, the benefits posed by these technologies may be outweighed by significant consequences to the cyber security of the entire defence supply chain. Supply chains are complex Systems of Systems, and the introduction of an insecure technology into such a complex ecosystem may induce cascading system-wide failure, and have catastrophic consequences to military mission assurance. Subsequently, there is a need for an evaluative process to determine the extent to which a new technology will affect the cyber security of military supply chains. This work proposes a new model, the Military Supply Chain Cyber Implications Model (M-SCCIM), that serves to aid military decision makers in understanding the potential cyber security impact of introducing new technologies to supply chains. M-SCCIM is a multiphase model that enables understanding of cyber security and supply chain implications through the lenses of theoretical examinations, pilot applications and system wide implementations.

Field-Programmable Gate Arrays (FPGAs) are versatile, reconfigurable integrated circuits that can be used as hardware accelerators to process highly-sensitive data. Leaking this data and associated cryptographic keys, however, can undermine a system's security. To prevent potentially unintentional interactions that could break separation of privilege between different data center tenants, FPGAs in cloud environments are currently dedicated on a per-user basis. Nevertheless, while the FPGAs themselves are not shared among different users, other parts of the data center infrastructure are. This paper specifically shows for the first time that powering FPGAs, CPUs, and GPUs through the same power supply unit (PSU) can be exploited in FPGA-to-FPGA, CPU-to-FPGA, and GPU-to-FPGA covert channels between independent boards. These covert channels can operate remotely, without the need for physical access to, or modifications of, the boards. To demonstrate the attacks, this paper uses a novel combination of "sensing" and "stressing" ring oscillators as receivers on the sink FPGA. Further, ring oscillators are used as transmitters on the source FPGA. The transmitting and receiving circuits are used to determine the presence of the leakage on off-the-shelf Xilinx boards containing Artix 7 and Kintex 7 FPGA chips. Experiments are conducted with PSUs by two vendors, as well as CPUs and GPUs of different generations. Moreover, different sizes and types of ring oscillators are also tested. In addition, this work discusses potential countermeasures to mitigate the impact of the cross-board leakage. The results of this paper highlight the dangers of shared power supply units in local and cloud FPGAs, and therefore a fundamental need to re-think FPGA security for shared infrastructures.

The following topics are dealt with: diseases; medical signal processing; learning (artificial intelligence); security of data; blood; patient treatment; patient monitoring; bioelectric phenomena; biomedical electrodes; biological tissues.

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build three components. Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semihonest MPC protocols. The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications. Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security. The malicious security of the protocols output by Aramis relies on integrity of the hardware and semi-honest security of MPC. Moreover, our system matches the inference accuracy of plaintext TensorFlow.We experimentally demonstrate the power of our system by showing the secure inference of real-world neural networks such as ResNet50 and DenseNet121 over the ImageNet dataset with running times of about 30 seconds for semi-honest security and under two minutes for malicious security. Prior work in the area of secure inference has been limited to semi-honest security of small networks over tiny datasets such as MNIST or CIFAR. Even on MNIST/CIFAR, CrypTFlow outperforms prior work.

This paper is devoted to the choice and justification of a joint-level controller for a joint with intrinsic elasticity. Such joints show a number of advantages in terms of shock robustness, interaction safety, energy efficiency and so on. On the other hand, the addition of elastic element, i.e. a torsion spring, leads to oscillating behaviour. Thus, more elaborate controller structure is required. Active damping injection approach is chosen in this article to improve the joint performance and achieve smooth motion. A method to select controller gains is suggested as well which allows step-wise customization, by which either the settling time can be minimized or the motion can be made fully smooth. Finally, the controller performance is verified in simulation.